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https://issues.apache.org/jira/browse/FLINK-2259?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15253867#comment-15253867
]
ASF GitHub Bot commented on FLINK-2259:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1898#discussion_r60732832
--- Diff:
flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/preprocessing/Splitter.scala
---
@@ -0,0 +1,215 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
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+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.ml.preprocessing
+
+import org.apache.flink.api.common.typeinfo.{TypeInformation,
BasicTypeInfo}
+import org.apache.flink.api.java.Utils
+import org.apache.flink.api.scala. DataSet
--- End diff --
whitespace between `scala.` and `DataSet`
> Support training Estimators using a (train, validation, test) split of the
> available data
> -----------------------------------------------------------------------------------------
>
> Key: FLINK-2259
> URL: https://issues.apache.org/jira/browse/FLINK-2259
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Theodore Vasiloudis
> Assignee: Trevor Grant
> Priority: Minor
> Labels: ML
>
> When there is an abundance of data available, a good way to train models is
> to split the available data into 3 parts: Train, Validation and Test.
> We use the Train data to train the model, the Validation part is used to
> estimate the test error and select hyperparameters, and the Test is used to
> evaluate the performance of the model, and assess its generalization [1]
> This is a common approach when training Artificial Neural Networks, and a
> good strategy to choose in data-rich environments. Therefore we should have
> some support of this data-analysis process in our Estimators.
> [1] Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of
> statistical learning. Vol. 1. Springer, Berlin: Springer series in
> statistics, 2001.
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